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Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels

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Abstract

Native mucus is polymer-based soft-matter material of paramount biological importance. How non-Gaussian and non-ergodic is the diffusive spreading of pathogens in mucus? We study the passive, thermally driven motion of micron-sized tracers in hydrogels of mucins, the main polymeric component of mucus. We report the results of the Bayesian analysis for ranking several diffusion models for a set of tracer trajectories [C. E. Wagner et al., Biomacromolecules, 2017, 18, 3654]. The models with “diffusing diffusivity”, fractional and standard Brownian motion are used. The likelihood functions and evidences of each model are computed, ranking the significance of each model for individual traces. We find that viscoelastic anomalous diffusion is often most probable, followed by Brownian motion, while the model with a diffusing diffusion coefficient is only realised rarely. Our analysis also clarifies the distribution of time-averaged displacements, correlations of scaling exponents and diffusion coefficients, and the degree of non-Gaussianity of displacements at varying pH levels. Weak ergodicity breaking is also quantified. We conclude that—consistent with the original study—diffusion of tracers in the mucin gels is most non-Gaussian and non-ergodic at low pH that corresponds to the most heterogeneous networks. Using the Bayesian approach with the nested-sampling algorithm, together with the quantitative analysis of multiple statistical measures, we report new insights into possible physical mechanisms of diffusion in mucin gels.

Graphical abstract: Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels

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Publication details

The article was received on 14 Oct 2018, accepted on 23 Jan 2019 and first published on 29 Jan 2019


Article type: Paper
DOI: 10.1039/C8SM02096E
Citation: Soft Matter, 2019, Advance Article

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    Non-Gaussian, non-ergodic, and non-Fickian diffusion of tracers in mucin hydrogels

    A. G. Cherstvy, S. Thapa, C. E. Wagner and R. Metzler, Soft Matter, 2019, Advance Article , DOI: 10.1039/C8SM02096E

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